Reservoir Operation Rules for Controlling Algal Blooms in a Tributary to the Impoundment of Three

24 Pages·2014·2.95 MB·English

Water 2014, 6, 3200-3223; doi:10.3390/w6103200
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water
ISSN 2073-4441
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Article
Reservoir Operation Rules for Controlling Algal Blooms in
a Tributary to the Impoundment of Three Gorges Dam
Jijian Lian 1, Ye Yao 1, Chao Ma 1,* and Qizhong Guo 2
1 State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University,
Tianjin 300072, China; E-Mails: [email protected] (J.L.); [email protected] (Y.Y.)
2 Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey,
Piscataway, NJ 08854, USA; E-Mail: [email protected]
* Author to whom correspondence should be addressed; E-Mail: [email protected];
Tel.: [phone]; Fax: [phone].
External Editor: Miklas Scholz
Received: 30 July 2014; in revised form: 24 September 2014 / Accepted: 13 October 2014 /
Published: 22 October 2014

Abstract: Since the first impoundment of Three Gorges Dam in 2003, algal blooms occur
frequently in the near-dam tributaries. It is widely recognized that the impoundment-induced
change in hydrodynamic condition with the lower current velocity will make the
eutrophication problem even more severe when an excessive amount of nutrients is already
loaded into a reservoir and/or its tributaries. Operation tests carried out by Three Gorges
Corporation in 2010 point to some feasible reservoir operation schemes that may have
positive impacts on reducing the algal bloom level. In our study, an attempt is made to
obtain, through a numerical hydrodynamic and water quality modeling and analysis, the
reservoir operation rules that would reduce the level of algal blooms in the Xiangxi River
(XXR), a near-dam tributary. Water movements and algal blooms in XXR are simulated
and analyzed under different scenarios of one-day water discharge fluctuation or two-week
water level variation. The model results demonstrate that the reservoir operations can
further increase the water exchange between the mainstream of the Three Gorges Reservoir
(TGR) and the XXR tributary and thus move a larger amount of algae into the deep water
where it will die. Analysis of the model results indicate that the water discharge fluctuation
constituted of a lower valley-load flow and a larger flow difference for the short-term
operation (within a day), the rise in water level for the medium-term operation (e.g., over
Water 2014, 6 3201

weeks), and the combination of the above two for the long-term operation (e.g., over
months) can be the feasible reservoir operation rules in the non-flood season for TGR.
Keywords: Three Gorges Reservoir; Xiangxi River; reservoir operation rules; algal bloom;
density current

1. Introduction
Development of reservoirs with large regulation capacity is an effective way to utilize water
resources, produce hydropower energy and reduce flood risk. However, the hydrodynamic condition
after impoundment significantly changes from the natural state with lower water velocity and longer
residence time, resulting in deteriorated water quality and eutrophication problems [1,2]. For the
largest man-made reservoir system in the world, the Three Gorges Reservoir (TGR), according tothe

annual report of eco-environmental monitoring of TGR released by Executive Office of State Council
TGP Construction Committee [3], more than 30% of the monitoring sections in the major tributaries
are in the state of high eutrophication, and algal blooms occur in over 20 tributaries from March to
October every year, such as Xiangxi River (XXR), Tongzhuang River and Daning River. Algal blooms
occurred in XXR seriously affect local residents’ life with turbid and stinky water [4,5]. Harmful algal
blooms with toxins as a prime agent of water quality deterioration also result in a loss of water for
recreation and drinking [6–8]. In order to alleviate the increasing algal bloom problems in TGR, more
and more attention is being paid to the feasible solutions.
In the last decade, long-term field monitoring and researches of algal blooms in the near-dam
tributaries of TGR have been carried out by government, corporations, colleges and research
institutions [9–14]. Agreement is reached that the significant change in hydrodynamic condition is a
compounding factor for the more frequent occurrence of algal blooms after the impoundment of Three
Gorges Dam (TGD) with its effect on the transport of nutrient loads, water temperature structure and
environment for algae growth [1,9,15–17]. An operation test is carried out by Three Gorges
Corporation in 2010, and the result points to some feasible reservoir operation schemes that may have
positive impacts on reducing the algal bloom level in the near-dam tributaries. With the help of
measured data, influencing factors on water movement of the tributaries and their influence on algal
blooms are found. Ji [18] and Yang [2] point out that the reverse density flow produced by water level
fluctuation and water temperature difference both have positive impacts on water exchange and
interaction between the mainstream and the tributaries. Jiang [19] verifies the result and emphasizes that
thermal stratified flow by water temperature difference should be considered for the algal bloom
control. Then, analysis is conducted to validate that a wave-type water release operation is beneficial in
reducing algal bloom in the tributary [20]. Furthermore, a feasible water level process of TGR is also
demonstrated to be helpful for controlling phytoplankton blooms in the tributary bays by the analysis
of mixing depth [21]. Besides above, effects of environment factors and operation of TGR on algal
blooms in the near-dam tributaries are also discussed. Several numerical models have been developed to
predict the algal blooms in tributaries of TGR with analysis of hydrodynamic condition, nutrient
concentration, temperature, and light illumination [22–24]. Zhou [25,26] proposes that water exchange
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and interaction are enhanced by daily peak-load regulation between the mainstream and the XXR.
Ma [27,28] describes water movement in the tributaries under multi-timescale operations of TGR and
develops multi-objective operation rules based on one-dimensional numerical model in non-flood
season for TGR with water quality considered. With above pieces of work, some puzzles have been
clarified for a clearer understanding of the mechanism of algal blooms in TGR. However, the
relationship among operation of TGR, hydrodynamics condition and algal bloom still should be further
studied and quantified that will form the basis for proposing a feasible operation rule for TGR.
Therefore, an attempt is made in this study to obtain, through a numerical hydrodynamic and water
quality modeling and analysis, the reservoir operation rules that would reduce level of algal blooms in
the Xiangxi River (XXR), a near-dam tributary.
The paper is arranged as follows. In Section 2, the study area is introduced in detail. From
consideration of remarkable vertical flow structure of TGR as a river-type reservoir, a hydrodynamic
and water quality model for the mainstream of TGR and tributary XXR (TGR-XXR model) is
developed and calibrated based on the CE-QUAL-W2 model that has been applied successfully to
many lakes, estuaries and reservoirs [29–34]. In Section 3, water movement and algal bloom
process in XXR are simulated and analyzed under 11 different scenarios of short-term daily water
discharge fluctuations or medium-term water level variations using chlorophyll a as the indicator
for algal blooms. Then, operation rules for reducing level of algal blooms are proposed in the
concluding Section 4.
2. Materials and Methods
2.1. Study Area
The TGD is located in the middle reach of the Yangtze River in China, creating TGR with a total
storage capacity of 393 × 108 m3 and a regulation storage capacity of 221.5 × 108 m3. Backwater zone
length of TGR is 660 km from the dam, and the reservoir has a total drainage basin area of 55,742 km2.
Average annual runoff is 14,000 m3/s, and inflow ranges from 3000 m3/s to 30,000 m3/s normally
within a year. The normal reservoir level is 175.0 m and the flood control level is 145.0 m. XXR is one
of the largest near-dam tributaries of TGR in Hubei Province, located only 31.3 km upward from the
TGD (110°25'–111°06' E, 31°04'–31°34' N), which flows southwardly into the Yangtze River. The
mainstream of XXR is 33 km long and the total length of XXR is 97.4 km with a drainage basin area of
3099 km2. Average annual runoff is 65.5 m3/s, and inflow ranges from 20 m3/s to 200 m3/s normally
within a year. Figure 1 shows location of TGD and sketch map of the study area (river reaches in red).
2.2. Methodology
2.2.1. Model Introduction
CE-QUAL-W2 (W2) model, developed by the U.S. Army Corps of Engineers’ Waterways Experiment
Station, is a two-dimensional (longitudinal-vertical) hydrodynamic and water quality model [35]. Because
the model assumes lateral homogeneity, it is best suited for relatively long and narrow water bodies
exhibiting longitudinal and vertical water quality gradients, similar to the TGR. Water surface
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elevations, velocities and temperatures can be predicted well by the hydrodynamic module of W2
model. Furthermore, 28 constituents and over 60 derived variables can be included in the water quality
module, by which the whole process of algal blooms and the transport of algae can be simulated with
integrity and clarity.
Figure 1. Location of the Three Gorges Dam (TGD) and sketch map of the study area.

2.2.2. Model Construction
The TGR-XXR model is set up with two water bodies (the mainstream is wb_1 and XXR is wb_2)
and eight reaches (seven reaches for the mainstream and one for XXR) according to the variation of
bottom slope. The mainstream is 598.5 km long upwards from the TGD, and the XXR is 32 km long
upwards from the confluence. Based upon the reservoir geometry and the computational efficiency and
desirable accuracy, the model is simulated with 213 longitudinal segments, each 4.5 km long for wb_1
and 0.5 km long for wb_2. Depending on the local water depth, up to 107 vertical layers are used at
each segment with a layer thickness of 2 m. Figure 2 shows the model segmentations, and the typical
segments that could be used in the model calibration and application are also labeled in it. A maximum
time step of 48 s is adopted for the model simulation.
Figure 2. Model segmentations of TGR-XXR. (a) Sketch map of grid cross-cut of XXR;
(b) Grid straight-cut of XXR; (c) Model grid plan and location of typical grids. (Number
in the inset of Figure 2c represents the model segment number, e.g., 137 represents
Segment 137).
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Figure 2. Cont.

Input data for the model simulations include reservoir or river bathymetry, meteorological data
(air temperature, dew point temperature, wind speed, wind direction, and percentage of cloud
cover), inflow and outflow characterized by flow rate or water level, water temperature and
nutrient concentration. The important calibration coefficients for the hydrodynamic module and
water quality module are listed in Table 1 and their values are based upon those recommended in
the past studies [35–38]. Among these calibration coefficients, Manning’s friction factor, maximum algal
growth rate, ammonium decay rate, and nitrate decay rate are most sensitive for the model results.
Table 1. Important coefficient for TGR-XXR model.
Coefficient Units Calibration Value
Manning’s friction factor of the mainstream - 0.04 a
Manning’s friction factor of XXR - 0.025 ~ 0.023 b
longitudinal eddy viscosity m2/s 1.0 c
longitudinal eddy diffusivity m2/s 1.0 d
Extinction for pure water m−1 0.25 c
Maximum algal growth rate day−1 1.2 c
Maximum algal respiration rate day−1 0.04 c
Maximum algal excretion rate day−1 0.04 c
Maximum algal mortality rate day−1 0.1 c
Algal settling rate m·day−1 0.1 c
Algal half-saturation for phosphorus limited growth g·m−3 0.003 c
Algal half-saturation for nitrogen limited growth g·m−3 0.14 c
Light saturation intensity at maximum photosynthetic rate W·m−2 75 c
Ammonium decay rate day−1 0.01 c
Nitrate decay rate day−1 0.1 c
Notes: a [36]; b [37]; c [35]; d [38].
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2.2.3. Field Data Collection
The TGR-XXR model is calibrated with field data collected from 10 October 2008 to 15 June 2009
(Julian Day: 284–532). The meteorological data is the daily average of monitoring data at Zigui station
obtained from the Meteorological Administration of Hubei Province. The inflow, outflow and water
temperature are the daily data, which are provided by China Three Gorges Corporation obtained from
the long-term field monitoring. China Three Gorges University carries out the field observation in
XXR monthly and provides the constituent concentrations over the model calibration period.
2.2.4. Hydrodynamic Model Calibration
Simulation results of water surface elevation and water temperature are used to calibrate the
hydrodynamic module of model, as shown in Figures 3 and 4. The statistic errors, mean absolute error
(MAE) and Nash-Sutcliffe efficiency (NSE) [39] are listed in Table 2. Figure 3 and the statistical
errors show that the modeled water surface elevation matches the measured data well, which rises from
the initial 155.59–172.76 m and then falls to 145.93 m. The comparisons of vertical profiles of water
temperature at the three typical stations from November to April depicted in Figure 4 as well as the
associated statistical errors show good model skill in reproducing the thermal structure of the water
column. Thermal stratification is weak in the mainstream of TGR but relatively strong in XXR, which
matches well with the actual situation of TGR.
Table 2. Results of statistic errors.
Statistic Water Surface
T PO NO -N NH -N DSi Chla
4 3 4
Error Elevation
0.416 0.014 0.108 0.064 0.849 5.531
MAE 0.077 (m)
(°C) (g/m3) (g/m3) (g/m3) (g/m3) (μg/L)
NSE 0.999 0.977 0.521 0.751 0.910 0.983 0.718
Figure 3. Comparison of modeled and observed water surface elevation for TGR (location
before TGD site, Segment 146 in model).
175
observed
modeled
170
)
m
( 165
n
o
ati
ev 160
El
e
c
fa 155
r
u
S
r
ate 150
W
145
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
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Figure 4. Comparison of modeled and observed vertical temperature profiles.

2.2.5. Water Quality Model Calibration
The procedure used for the water quality module calibration searches for the best fit between the
observed and predicted constituents including nitrogen (ammonia and nitrate), phosphorus, dissolved
silica and chlorophyll a (Chla). The dominant species of algal blooms in XXR varies seasonally, and
diatom is typically the dominant species of the spring algal bloom [3]. Because algal blooms do not
occur in the mainstream, the calibration results shown here are only for the XXR. Comparisons of
temporal variations between modeled nutrient and observed data at a typical station (Segment 176) in
the upper middle reach are shown in Figure 5, and the calibration results of Chla at different stations
are shown in Figure 6, with the statistic errors listed in Table 2. Visual observations and the statistic
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errors indicate that the model performed well in simulating the water quality of the mainstream of
TGR and XXR.
Figure 5. Comparisons of modeled and observed nutrient concentrations (Segment 176).
0.30
modeled
observed
0.25
0.20
3m) 0.15
g/
O (4
P 0.10
0.05
0.00
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
1.8
modeled
1.6 observed
1.4
1.2
3m) 1.0
g/
N ( 0.8
O-30.6
N
0.4
0.2
0.0
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
1.0 modeled
observed
0.8
3m) 0.6
g/
N ( 0.4
H-4
N
0.2
0.0
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
30
modeled
observed
25
20
3m)
15
g/
Si (
D 10
5
0
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
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Figure 6. Comparisons of modeled and observed Chla concentration.
80
modeled Segment 211
observed
60
L) 40
g/
μ
a (
Chl 20
0
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
100
modeled Segment 202
observed
80
L) 60
g/
μ
a ( 40
hl
C
20
0
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
120
modeled Segment 176
100 observed
80
L)
g/ 60
μ
hla ( 40
C
20
0
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
120
modeled Segment 163
100 observed
80
L)
g/ 60
μ
(
a 40
hl
C
20
0
280 300 320 340 360 380 400 420 440 460 480 500 520 540
Julian Day
Water 2014, 6 3209

3. Reservoir Operation Rules for Controlling Algal Blooms
3.1. Generalization of Water Discharge Process of TGHS
According to the actual regulation rules, the Three Gorges Hydropower Station (TGHS) plays
roles in both electricity base generation and peak-load regulation. For the flood-control,
environment-maintenance, and other purposes, reservoir level is set to fall from the normal level of
175 m in November to the flood control level of 145 m in May or June of the following year, but it
should be higher than 155.0 m before mid-May. After the impoundment of TGD, sedimentation rate at
from the upstream end increases. Clear water released from TGHS and Gezhouba Hydropower Station
aggravates the river bed erosion, especially in the downstream of Gezhouba Reservoir [40]. In
addition, low water level has a negative effect on navigation and water supply. In order to solve the
problem, average daily water discharge of TGHS is constrained to be more than 5000 m3/s in the
on-flood season. During the non-flood season as well as the algal bloom season from March to June,
inflow to TGR is larger than 5000 m3/s, thus it is feasible to utilize the excess flow/volume to
implement a reservoir regulation with a water level fluctuation for weeks.
Water discharge processes of TGHS are generalized into short-term operation (daily operation),
medium-term operation (approximate two weeks) and long-term operation (for the non-flood season).
For the short-term operation, due to an important role of power generation and water supply to
downstream river, daily water discharge process of TGHS in non-flood season is planned according to
daily inflow, release requirement and load demand with peak-load regulation. In this study, 24 h of
a day are divided into four typical periods and four transition periods with a total of 48 time intervals
(each lasts 0.5 h), including valley-load period (when the discharge is the lowest) (0:00–6:00), morning
peak-load period (8:00–11:00), medium-load period (12:00–17:00), and evening peak-load period
(19:00–22:00). Water discharge does not change over a typical period but changes linearly over
a transition period. Water discharge in the evening peak-load period (peak-load flow) is equal to that in
the valley-load period (valley-load flow) plus flow difference (Figure 7). Water discharges in the
morning peak-load period or the medium-load period are obtained from its ratio over that in evening
peak-load period, set to be 0.85 and 0.65, respectively. For the medium-term operation, the water level
variation over a period of time is set to be three different scenarios: constant, rise or fall (Figure 8).
Additionally, the long-term operation is planned step by step with periodical medium-term operation
and short-term operation for a month approximately in the non-flood season. Here, the long-term
operation rules are obtained from the inference based on the above two operation rules without
individual simulation.
3.2. Scenario Construction
The calibrated model is applied to predict the influence of different timescale operation on algal
blooms in XXR using Chla concentration as an indicator that is common for algae biomass evaluation [41].
Forty days (Julian Day: 0–40) are set as the simulation period in order to model an entire algal bloom
process probably occurred in spring (from March to May) in XXR based on the actual situation.
To separate the effects of reservoir regulation from the effects of other variables, all other variables are
set to have constant values. The air temperature and dew point temperature are set to be constant at 18 °C